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The prediction of multiaxial fatigue probabilistic stress–life curve by using fuzzy theory

Published online by Cambridge University Press:  04 May 2017

Bochuan Li ,
Chao Jiang ,
Xu Han  and
Yuan Li
Bochuan Li
Affiliation:
State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha City, People's Republic of China Faculty of Engineering, Kyushu University, Nishi-ku, Fukuoka-shi, Fukuoka, Japan
Chao Jiang*
Affiliation:
State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha City, People's Republic of China
Xu Han
Affiliation:
State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha City, People's Republic of China
Yuan Li
Affiliation:
Department of Traffic and Transportation Engineering, College of Basic Education, National University of Defense Technology, Changsha City, People's Republic of China
*
Reprint requests to: Chao Jiang, State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha City, People's Republic of China410082. E-mail: [email protected]
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Abstract

The fuzziness of the traditional multiaxial fatigue prediction model is discussed and the fuzzy theory is applied into fatigue reliability analysis. The fuzzy linear regression analysis method is used to determine the fuzzy coefficients in the multiaxial stress–life equation under a small sample condition, and the corresponding multiaxial fatigue probabilistic stress–life curve is calculated with different confidence levels.

Keywords

Fuzzy TheoryMultiaxial Fatigue PredictionMultiaxial Probabilistic Stress–Life CurveReliability Analysis
Type
Special Issue Articles
Information
AI EDAM , Volume 31 , Special Issue 2: Uncertainty Quantification for Engineering Design , May 2017 , pp. 199 - 206
Copyright
Copyright © Cambridge University Press 2017 

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